Neural-Symbolic Learning and Reasoning: A Survey and Interpretation https://arxiv.org/abs/1711.03902 The study and understanding of human behaviour is relevant to computer science , artificial intelligence, neural computation, cognitive science, philosophy, psychology, and several other areas. Presupposing cognition as basis of behavi our, among the most prominent tools in the modelling of behaviour are computat ional-logic systems, connectionist models of cognition, and models of uncertai nty. Recent studies in cognitive science, artificial intelligence, and psychol ogy have produced a number of cognitive models of reasoning, learning, and lan guage that are underpinned by computation. In addition, efforts in computer sc ience research have led to the development of cognitive computational systems integrating machine learning and automated reasoning. Such systems have shown promise in a range of applications, including computational biology, fault dia gnosis, training and assessment in simulators, and software verification. This joint survey reviews the personal ideas and views of several researchers on n eural-symbolic learning and reasoning. The article is organised in three parts : Firstly, we frame the scope and goals of neural-symbolic computation and hav e a look at the theoretical foundations. We then proceed to describe the reali sations of neural-symbolic computation, systems, and applications. Finally we present the challenges facing the area and avenues for further research. --

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